After studying physics, history and philosophy at the Universities of Giessen and Heidelberg, Professor Rost received his doctorate at the European Molecular Biology Laboratory (EMBL) in 1994. Following research stays at EMBL and the European Bioinformatics Institute in Cambridge (UK), as well as a brief period in industry at LION Bioscience in Heidelberg, he assumed a professorship at Columbia University (New York) in 1998. In 2009, he accepted an appointment to the Chair of Bioinformatics at TUM. He is a member of the New York Academy of Sciences and has been President of the International Society for Computational Biology since 2007. He has authored 200 scientific publications with a Hirsch index of 50 (2010).
Professor Rost conducts research on bioinformatics and computer-aided biology, with a focus on predicting the functions and structures of proteins and genes. His particular interest is predicting protein interactions and the effects of changing individual amino acids, with the goal of fostering a better understanding of how proteins, genes and cells work. He also focuses on enabling earlier diagnosis and more effective treatment of illnesses. The specific niche of his research group links artificial intelligence and machine learning to evolution.
The objective of our group is to predict aspects of protein function and structure from sequence. The wealth of evolutionary information available through comparing the whole bio-diversity of species makes such an ambitious goal achievable. Our particular niche is the combination of evolutionary information with machine learning. We develop the in silico prediction of protein interactions, including networks, sub-cellular localization, and functional classifications such as EC and GO numbers from sequence. In this talk I will focus on predictions of protein localization and protein-protein interactions and lessons learned from those predictions. I will also present the concept of the Dark Proteome and how protein disorder appears to play a unique role in evolution.
Our group introduced the combination of evolutionary information with machine learning. The wealth of evolutionary information available through the comparison of the whole bio-diversity of species helps us to develop methods that predict aspects of protein structure and function from sequence. In this talk, I will focus on methods that predict the effect of single amino acid variants (SAVs/nsSNPs) upon molecular function and the organism. One application of such methods is to predict the effects of all possible SAVs. The resulting landscape of complete in silico mutagenesis (or deep mutational scanning) describes how a protein is susceptible to change is becoming an important feature to foster the understanding of the molecular details of protein function. Predicting the effects of sequence variants has also become important for medical research and is an important challenge toward advancing personalised health. I will present some surprising results from such prediction methods for the analysis of large populations.